46 results about "Nmr data" patented technology

The invention discloses a shale oil reservoir CO2 huff and puff miscible-phase region dynamic discrimination method. The method comprises the steps that nuclear magnetic resonance testing is conducted on a target oil reservoir core sample; the method comprises the following steps: scanning a target oil reservoir core sample, constructing a three-dimensional core pore structure model, introducing nuclear magnetic resonance data to calibrate initial fluid saturation, and establishing a dynamic displacement numerical simulation model in combination with a CO2-crude oil phase equation, an interfacial tension model and a core permeability heterogeneity parameter; designing a plurality of groups of huff and puff experiments, simulating a CO2 injection process, monitoring nuclear magnetic resonance signal changes of the rock core in a displacement process in real time, correcting fluid saturation, a pressure field and interfacial tension parameters in a dynamic displacement numerical simulation model, and dividing a miscible-phase region; and selecting an optimal development parameter combination based on mixed-phase region distribution under different transfer injection opportunities, soaking time and throughput turns. According to the method, accurate division and dynamic discrimination of the CO2 huff and puff mixed-phase region are realized.

The invention provides a traditional Chinese medicine preparation quality detection method and system based on multi-dimensional data analysis, and belongs to the technical field of traditional Chinese medicine preparation quality detection. The method comprises the following steps: acquiring mass spectrum data and nuclear magnetic resonance data of various active components in a preparation to generate a multi-dimensional chemical characteristic data set; synchronously collecting corresponding data of the medicinal raw materials, and constructing a process and environment association database; constructing a chemical and process coupling analysis model; and monitoring the production process in real time to obtain the dynamic chemical fingerprint spectrum. By constructing a full-component chemical map and associating the full-component chemical map with process and environmental parameters and combining clinical curative effect data to establish a drug effect and component response curved surface model, the limitation that only a few index components are concerned in traditional detection is broken through, so that a detection result can accurately reflect the multi-target and overall regulation characteristics of the traditional Chinese medicine preparation, and the detection accuracy is improved. The problem that the components are qualified but the curative effect is poor due to single detection dimension is avoided, and the relevance between quality detection and the clinical curative effect is remarkably improved.

The invention discloses a rapid marinating soup quality parameter acquisition method and a marinating time optimization method in a sauced product cooling process, and belongs to the technical field of sauced product production processes, and the method comprises the following steps: S1, acquiring near infrared spectrum data of a meat sample by adopting a near infrared spectrum technology; performing two-dimensional correlation analysis on the near infrared spectrum data, converting the one-dimensional spectrum data into two-dimensional correlation spectrum data, and improving the resolution of the one-dimensional spectrum data and the separation degree of overlapping peaks to obtain a two-dimensional correlation analysis result; s2, processing the two-dimensional correlation spectrum data by using a moving window technology, observing a time period in which a functional group changes along with external interference, and converting static detection of a substance into dynamic analysis to obtain a moving window technology processing result; and S3, acquiring nuclear magnetic resonance data of the meat sample by adopting a nuclear magnetic resonance technology, and reflecting the motion state of hydrogen protons. According to the invention, the meat moisture migration and fat oxidation degree can be accurately identified.

This application discloses a downhole nuclear magnetic resonance (NMR) inversion method, apparatus, equipment, and storage medium based on pruned Bayesian methods, relating to the field of NMR data processing technology. The method includes: constructing a forward dataset based on fluid relaxation time and acquisition parameters, and adding Gaussian white noise to obtain the inversion input signal; initializing prior parameters, posterior mean, and variance; constructing a kernel matrix based on relaxation time points; constructing a kernel-dictionary matrix using Gaussian functions; updating the fitted echo data and fitting residuals using hierarchical Bayesian methods, thereby updating the prior parameters; updating the posterior mean through evidence maximization and calculating the proportion of each fluid component; pruning the kernel-dictionary matrix according to a preset pruning threshold and proportion, iterating until a termination condition is met, and finally mapping to obtain a two-dimensional NMR relaxation spectrum. This method solves the problem of waiting time and loss of diffusion relaxation dimension information caused by improved logging efficiency, which makes accurate multidimensional NMR logging inversion difficult.

The method for analyzing the content of D-lactic acid repeating units in polylactic acid of the present disclosure is characterized in that without special chemical treatment of polylactic acid, the content of D in polylactic acid can be quickly and accurately analyzed by using NMR data of polylactic acid and hypothetical polylactic acid.

A system and method include an analysis control unit configured to receive nuclear magnetic resonance (NMR) data of a sample. A reference database is in communication with the analysis control unit. The reference database stores standard data including information regarding a plurality of standards for chemical compounds, and may also store information regarding quantum mechanical spectral analysis (QMSA) model data and test method data. The analysis control unit is further configured to compare the NMR data with the standard data to determine if the NMR data is associated with one of the plurality of standards, and identify one or more differences between the NMR data and the one of the plurality of standards.

Systems and techniques are provided for determining a time zero echo of a nuclear magnetic resonance (NMR) sequence. An example method includes obtaining, via an NMR tool in a borehole, echo waveforms associated with refocusing pulses and a free induction decay (FID) waveform associated with an excitation pulse; determining echo values based on the echo waveforms and an apparent time-zero echo value based on the FID waveform, the apparent time-zero echo value representing a time zero echo; applying a correction factor to the apparent time-zero echo value to yield a corrected time zero echo value; and determining a spectrum associated with a sample based on an inversion performed on the corrected time zero echo values and the set of echo values before or after a conversion of the corrected time zero echo value and the set of echo values to porosity units, the conversion of the corrected time zero echo value and the set of echo values being based on one or more conversion factors.

The invention provides a dual-channel self-supervision pre-training method for NMR (nuclear magnetic resonance) one-dimensional spectrogram signals, which comprises the following steps of: after L2 standardization and water peak removal are performed on the NMR signals, constructing a dual-task MAE framework, inputting the first channel into Transformer by adopting Patch Embedding, and inputting the second channel into a shared encoder after extracting features through a four-stage CNN (convolutional neural network) containing InstanceeNorm1d; the double decoders reconstruct the original input respectively, wherein the CNN path uses a deconvolution network to complete up-sampling; the loss is dynamically fused by learnable weights. Meanwhile, the invention further provides a training model generated under the training method and a corresponding computer medium, by adopting InstanceNorm1d, the stability of a small batch is improved, a signal physical structure is reserved by using L2 standardization, the representation learning ability of label-free NMR data is effectively improved, and the method is suitable for downstream tasks such as disease detection and the like.

The present invention provides a multi-dimensional nuclear magnetic resonance method for rapidly evaluating organic matter in shale. The method includes the following steps: designing a nuclear magnetic resonance pulse sequence and collecting data to obtain nuclear magnetic resonance data; processing the nuclear magnetic resonance data to obtain T<subgt;1< / subgt>-T<subgt;2< / subgt;<supgt;*< / supgt> imaging; interpreting the nuclear magnetic resonance data and correlating it with organic matter. Further, a device for rapidly evaluating organic matter in shale is provided. This application can rapidly evaluate the distribution and overall content of organic matter in shale, and can provide rapid imaging for nuclear magnetic resonance instruments with different field strengths and different configurations, and is used to precisely measure the organic matter content and spatial distribution in samples of tight shale oil and gas reservoirs. This application can rapidly and non-destructively analyze to obtain the T<subgt;1< / subgt> and T<subgt;2* values and imaging results of tight shale samples, and further can characterize important information such as the internal structure, micropore characteristics, and occurrence status of saturated fluids in the samples, and has important application value in the analysis of complex samples in unconventional oil and gas reservoirs.

The invention discloses a low-field NMR relaxation time correlation spectrum reconstruction method based on deep learning, and the method comprises the steps: obtaining a sample pair which comprises original TD-NMR data, corresponding noiseless TD-NMR data and an NMR relaxation time correlation spectrum label, building a deep neural network model which comprises a convolution denoising module and a GViT inversion module, and carrying out the reconstruction of a low-field NMR relaxation time correlation spectrum. A total loss function including denoising loss, inversion loss and forward modeling loss is constructed, and robust reconstruction from original TD-NMR data with a low signal-to-noise ratio to a high-precision two-dimensional NMR relaxation time correlation spectrum is achieved. According to the method, the problems of high calculation cost, long time consumption and poor reconstruction effect on the two-dimensional relaxation time correlation spectrum of the low-signal-to-noise-ratio TD-NMR data in the traditional inversion technology are effectively solved.

The invention provides a three-dimensional nuclear magnetic resonance detection device, system and method, and relates to the technical field of porous cultural relic detection, the device comprises a frame structure, a nuclear magnetic resonance probe, a displacement module, an industrial control computer and an electron spectrometer module; the nuclear magnetic resonance probe is arranged on the displacement module and used for collecting nuclear magnetic resonance data of the porous cultural relics; the displacement module is arranged on the front side of the frame structure and is used for driving the nuclear magnetic resonance probe to move; the back side of the frame structure is provided with an industrial control computer and an electron spectrometer module, and the industrial control computer is electrically connected with the electron spectrometer module and used for sending out a control instruction and receiving, processing and displaying data; the electron spectrometer module is electrically connected with the nuclear magnetic resonance probe and the displacement module and used for sending instructions to the nuclear magnetic resonance probe and the displacement module and transmitting collected detection data to the industrial control computer. The weathering and erosion degrees in the range from the surface of the porous cultural relic to the shallow layer are quantitatively described, and data support is provided for later cultural relic protection and cultural relic restoration.

A method for establishing mathematical model of relationship between spontaneous imbibition volume and time of porous medium includes sample pretreatment, fully-saturation and centrifugal experiments and NMR T2 measurement. First, two rock core samples of predetermined size are selected for cleaning and drying. The first rock sample is vacuumed and injected with water to obtain a saturated sample for NMR T2 measurement. Then, spontaneous imbibition experiment is conducted on another sample, and T2 measurements are conducted to obtain the water distribution and migration characteristics during the imbibition process. Next, the calculation of the imbibition permeability, average capillary pressure and surface relaxivity are conducted based on the NMR data obtained from two samples. Finally, substitute these parameters into the Handy relationship to obtain a new NMR-based mathematical spontaneous imbibition model.

There are provided systems, methods, and processor-readable media for optimizing the end to end operation of a hydrocarbon processing system using NMR spectroscopy data. The hydrocarbon processing system is configured to process hydrocarbon feedstock material, via a configuration defined by a set of configuration parameters, such that one or more final products are produced. A yield prediction model is used to process NMR data obtained from an NMR scan of the feedstock material to generate yield prediction data. The yield prediction data includes predictions of product yields of various intermediate products and / or final products. An optimization module is used to process the yield prediction data predicted by the trained yield prediction model such that optimized configuration parameter data is obtained. The optimized configuration parameter data is effective for establishing an optimized configuration for operating the hydrocarbon processing system that optimizes or improves an objective metric.

Provided is a method for analyzing the content of D-lactic repeating units (D content) in polylactic acid that has a feature that the D content in polylactic acid can be quickly and accurately analyzed by using NMR data of polylactic acid and hypothetical polylactic acid without special chemical treatment for polylactic acid.

Nuclear magnetic resonance (NMR) tools collect data by transmitting excitation pulses and receiving signals stimulated by the transmitted excitation pulses. Excitation pulses are transmitted by the NMR tool at a time referred to as echo time zero. After a nuclear magnetic resonance (NMR) tool transmits excitation pulses, NMR signals stimulated by those excitation pulses are received at the NMR tool. Received signals include a ringing portion and an echo waveform. This ringing portion can prevent the NMR tool from collecting echo time zero data that is useful for determining properties of materials adjacent to the NMR tool. Systems and techniques of the present disclosure determine apparent time-zero echo values such that determinations made from collected NMR data can be more accurate.

The invention discloses a compound structure analysis method and system based on nuclear magnetic resonance data, and the method comprises the steps: receiving original nuclear magnetic resonance data, collected by a nuclear magnetic instrument, of a to-be-analyzed compound, preprocessing the original nuclear magnetic resonance data so as to convert the nuclear magnetic resonance data in a time domain form into nuclear magnetic resonance data in a frequency domain form, suppress noise interference and output a peak position list; constructing a solvent peak template matched with an actual spectrogram peak shape in the nuclear magnetic resonance data, delimiting candidate areas in the peak position list according to a configured search interval, and matching a signal of each candidate area with a signal of the solvent peak template to obtain a solvent peak; removing the solvent peak, calculating the offset between the actual chemical shift and the theoretical value, applying the offset to global chemical shift correction to obtain a chemical shift list, and performing structural analysis on the to-be-analyzed compound based on the chemical shift list. According to the invention, the problem of poor accuracy during compound structure analysis in the prior art is solved.

1. Name of the designed product: multi-channel electronic spectrometer. 2. Use of the designed product: for nuclear magnetic resonance data acquisition, simultaneously acquiring multiple data to K-space, parallel receiving and processing multi-frequency signals from different atomic nuclei. 3. Design points of the designed product: in shape. 4. Picture or photo that best shows the design points: front view.

The application discloses a three-dimensional nuclear magnetic resonance rock sample fluid experiment analysis method and device based on a static gradient magnetic field, and the method comprises the following steps: performing fluid saturation treatment on a to-be-detected core, placing the treated to-be-detected core in a preset environment condition, and simulating fluid occurrence states in a reservoir under different diffusion time conditions; performing nuclear magnetic resonance experiments on the to-be-detected core based on the static gradient magnetic field at each diffusion time point, and collecting nuclear magnetic resonance echo data of the to-be-detected core at multiple different time points; performing three-dimensional inversion calculation on the nuclear magnetic resonance echo data, obtaining T1-T2-t three-dimensional distribution data, and performing visual analysis on the T1-T2-t three-dimensional distribution data, so as to represent the space-time variation characteristics of the reservoir fluid. The application combines the diffusion process of the fluid with the nuclear magnetic resonance T1-T2 distribution, forms three-dimensional nuclear magnetic resonance data, and can effectively capture the space-time variation of the complex fluid occurrence state in the unconventional oil and gas reservoir.

Systems and techniques are provided for determining a time zero echo of a nuclear magnetic resonance (NMR) sequence. An example method includes obtaining, via an NMR tool in a borehole, echo waveforms associated with refocusing pulses and a free induction decay (FID) waveform associated with an excitation pulse; determining echo values based on the echo waveforms and an apparent time-zero echo value based on the FID waveform, the apparent time-zero echo value representing a time zero echo; applying a correction factor to the apparent time-zero echo value to yield a corrected time zero echo value; and determining a spectrum associated with a sample based on an inversion performed on the corrected time zero echo values and the set of echo values before or after a conversion of the corrected time zero echo value and the set of echo values to porosity units, the conversion of the corrected time zero echo value and the set of echo values being based on one or more conversion factors.

A method for generating a property representation of a substance is described. The method comprises: obtaining portions of one or more nuclear magnetic resonance (NMR) data of the substance, where each NMR data portion comprises one or more NMR datasets, each NMR dataset comprising intensity data and relaxation data; processing each NMR data portion into a multi-dimensional distribution of a fitting data set by performing a plurality of fits of a function, where the function includes at least 3 fitting parameters; and generating a characteristic representation of the substance from the fitting data set. The processing includes applying at least one initial guess value. Each set of fitting data comprises data embedded with at least one subset of fitting data comprising an apparent longitudinal relaxation time (T1 *), an associated apparent transverse relaxation time (T2 *) and an associated intensity (M0), preferably said function being a function of a model representing the obtained NMR data portion.

The invention belongs to the technical field of medical examination, and relates to a drug sensitivity prediction method based on a blood NMR (nuclear magnetic resonance) editing spectrum, for example, a cancer immunotherapy drug-immune checkpoint inhibitor (ICIs) with ubiquitous drug resistance. The method comprises the following steps: firstly, acquiring pathological indexes before and after drug treatment and NMR (nuclear magnetic resonance) editing spectrum fingerprint data of plasma or serum, then constructing a drug sensitivity prediction model and screening predictive biomarkers by adopting a machine learning algorithm, and finally performing drug sensitivity prediction on blood NMR data of a patient needing to be treated by using the biomarkers. The NMR editing spectrum adopted by the invention is non-destructive to a sample, interaction information of a drug and an organism is retained while large and small molecules in blood are comprehensively observed, uncertainty caused by prediction based on upstream data sets such as genomics and the like can be avoided by blood metabolism phenotype analysis, prediction accuracy is improved, and the method has a good application prospect. The method is expected to be applied to clinical medication guidance of drugs with drug resistance.

This invention discloses a method for dynamically identifying miscible regions in CO2 huff and puff of shale oil reservoirs, comprising: performing nuclear magnetic resonance (NMR) testing on core samples from the target reservoir; scanning the core samples to construct a three-dimensional core pore structure model, and importing NMR data to calibrate the initial fluid saturation; establishing a dynamic displacement numerical simulation model by combining the CO2-crude oil phase equation, interfacial tension model, and core permeability heterogeneity parameters; designing multiple huff and puff experiments to simulate the CO2 injection process, while simultaneously monitoring the changes in the core's NMR signal during displacement in real time, correcting the fluid saturation, pressure field, and interfacial tension parameters in the dynamic displacement numerical simulation model, and dividing the miscible regions; and selecting the optimal combination of development parameters based on the distribution of miscible regions under different injection timing, well shut-in time, and huff and puff cycles. This invention achieves accurate division and dynamic identification of miscible regions in CO2 huff and puff.

The invention provides a medical instrument department room acceptance method and system, and can be applied to the technical field of nuclear magnetic resonance and environment monitoring. The method comprises the following steps: injecting a nuclear magnetic resonance gas probe into a to-be-evaluated space by using a gas probe injector to form environmental disturbance of the nuclear magnetic resonance gas probe and the to-be-evaluated space; performing nuclear magnetic resonance data acquisition on the to-be-evaluated space by using a first nuclear magnetic resonance system arranged in the to-be-evaluated space to obtain a nuclear magnetic resonance spectrum sequence of each space unit; performing nuclear magnetic resonance diffusion weighted data acquisition on the to-be-evaluated space by using a second nuclear magnetic resonance system arranged in the to-be-evaluated space to obtain a diffusion weighted imaging sequence of each space unit in the to-be-evaluated space; and performing environment state evaluation on the to-be-evaluated space by using a processor based on the nuclear magnetic resonance spectrum sequence of each space unit in the to-be-evaluated space and the diffusion weighted imaging sequence of each space unit in the to-be-evaluated space to obtain an evaluation result.

This application discloses a method, system, equipment, and medium for evaluating the construction quality of a crushed stone pile composite foundation. The method includes: performing nuclear magnetic resonance (NMR) measurements on a representative single pile to obtain NMR data, wherein the representative single pile is a representative crushed stone pile in the composite foundation; evaluating the physical properties of the representative single pile based on the NMR data; conducting a penetration test on the soil surrounding the representative single pile to evaluate the physical properties of the soil; determining whether there are any abnormal bodies in the composite foundation; if the physical properties of the representative single pile and the surrounding soil both meet the requirements, and there are no abnormal bodies in the composite foundation, then the construction quality of the composite foundation is deemed to meet the standards. Using the embodiments of this application, through NMR detection, penetration tests, and abnormal body detection, a comprehensive evaluation of the construction quality of the composite foundation can be conducted from multiple dimensions, improving the accuracy and reliability of the evaluation.

A nuclear magnetic resonance (NMR) system measures an effective osmolarity or tonicity and characterizes the effective osmolarity or tonicity's effect on water exchange in a specimen. The NMR system includes a magnetic resonance (MR) device, which has: a magnetic field generator; a magnetic field gradient generator; a radio frequency (RF) transmitter; and a RF receiver. The NMR system also includes a processing system coupled to the MR device. The processing system is configured to: receive sample NMR data from the MR device, the sample NMR data corresponding to the specimen; estimate an exchange rate from the NMR data, the exchange rate corresponding to the water exchange through a semipermeable membrane of the specimen; and estimate the effective osmolarity or tonicity based on the estimated exchange rate.

A method may include acquiring NMR data using a NMR unit disposed in a borehole in a formation, where the NMR data represent characteristics of the formation. The method may also include compressing the NMR data using projection followed by adaptive quantization to generate multiple, quantized data structures, where the adaptive quantization selects a gain value from a plurality of gain values. The method may further include transmitting the multiple, quantized data structures using borehole telemetry, where the multiple, quantized data structures include an indicator for the selected gain value.

The present application belongs to the technical field of medical examination, and relates to a drug sensitivity prediction method based on blood NMR editing spectrum, such as cancer immunotherapy drugs, immunological checkpoint inhibitors (ICIs), which are commonly resistant to drugs. The method first acquires pathological indexes and NMR editing spectrum fingerprint data of plasma or serum before and after drug treatment, then adopts a machine learning algorithm to construct a drug sensitivity prediction model and screen predictive biomarkers, and finally uses the biomarkers to predict the drug sensitivity of blood NMR data of patients in need of treatment. The NMR editing spectrum used in the present application is non-destructive to the sample, can comprehensively observe large and small molecules in the blood, retains the interaction information between the drug and the body, and the blood metabolism phenotype analysis can avoid the uncertainty caused by the prediction based on the upstream data set such as genomics, improve the prediction accuracy, and is expected to be applied to the clinical drug guidance of the drug-resistant drugs.

This invention discloses a rapid prediction system for crude oil SARA composition based on low-field nuclear magnetic resonance (NMR), relating to the fields of petrochemical analysis and energy resource development. The system workflow is as follows: Samples covering light, medium, heavy, and high-asphalt crude oil are prepared and placed in tubes; LF-NMR transverse relaxation time (T₂) spectra are acquired using CPMG sequences, and experimental parameters are standardized; the spectra are preprocessed, including normalization and noise reduction, and features are extracted; a partial least squares regression (PLSR) model is constructed using column chromatography measurements as a reference, and optimized through cross-validation and external validation; LF-NMR data of unknown samples are input, and the system rapidly outputs the contents of saturated hydrocarbons, aromatic hydrocarbons, resins, and asphaltenes. This system requires only 5-10 minutes for a single analysis, has a prediction R² exceeding 0.95, is adaptable to crude oils with different API strengths and viscosities, has no solvent contamination, can be deployed on-site, and is suitable for oilfield development, refining optimization, and crude oil grading and pricing scenarios.

To provide a method for determining the average number of first polymers covalently bound to a protein-first polymer conjugate in solution.SOLUTION: The present invention includes: a) performing measurement of 1H nuclear magnetic resonance on a solution containing a protein-first polymer conjugate; b) processing measurement values of the 1H nuclear magnetic resonance; c) integrating the measurement values of the 1H nuclear magnetic resonance at a first type of proton peak; d) calibrating the result of c); e) integrating the measurement values of the 1H nuclear magnetic resonance at a second type of proton peak, the second type of proton peak including a signal generated by at least one proton in the first polymer; f) dividing a result of e) by the number of second type protons per monomer of the first polymer; and g) dividing the result of f) by the average number of monomers in the first polymer.SELECTED DRAWING: Figure 1

The present disclosure provides a time-frequency domain joint reconstruction and reconstruction quality evaluation method of nuclear magnetic resonance spectrum, comprising the following steps: reconstructing according to undersampling data of simulation data; obtaining the uncertainty and Pearson correlation coefficient of the first reconstructed data; constructing a space rectangular coordinate system to form a point array of scattered points; reconstructing according to undersampling data of nuclear magnetic resonance data; obtaining the uncertainty of the second reconstructed data; using the approximate density to obtain the probability that the Pearson correlation coefficient is a higher value under the condition that the uncertainty is known; and evaluating the second reconstructed data. The present disclosure also provides a nuclear magnetic resonance spectrum reconstruction quality evaluation device, an electronic device and a readable storage medium. Based on the deep learning reconstruction algorithm of the time-frequency domain joint reconstruction, the present disclosure realizes the complementation and interaction of time-frequency domain information in the reconstruction process, completes the high-quality reconstruction of NMR spectrum under flexible sampling rate, sampling scheme and sampling size, and realizes the no-reference quality evaluation of the reconstruction result by constructing the reconstruction quality evaluation point array.